# Computations
import numpy as np
import pandas as pd

# preprocessing
from sklearn.impute import SimpleImputer
import re

# Visualisation libraries

## Text
from colorama import Fore, Back, Style
from IPython.display import Image, display, Markdown, Latex, clear_output

## plotly
from plotly.offline import init_notebook_mode, iplot 
import plotly.graph_objs as go
import plotly.offline as py
from plotly.subplots import make_subplots
import plotly.express as px

## seaborn
import seaborn as sns

## matplotlib
import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse, Polygon
from matplotlib.font_manager import FontProperties
plt.style.use('seaborn-whitegrid')
plt.rcParams['axes.labelsize'] = 14
plt.rcParams['xtick.labelsize'] = 12
plt.rcParams['ytick.labelsize'] = 12
plt.rcParams['text.color'] = 'k'
%matplotlib inline

## WordCloud
from wordcloud import WordCloud


import warnings
warnings.filterwarnings("ignore")

Home / Business Analytics and Modeling

Telco Customer Churn

• Preprocessing
• Exploratory Data Analysis
• Modeling: Decision Tree Classifier with Voting Classifier
• Modeling: PyTorch Multi-layer Perceptron (MLP) for Binary Classification

Telco Customer Churn

In this article, we analyze and predict customer churn for Telco Customer Churn data.

Dataset

• Customers who left within the last month – the column is called Churn
• Services that each customer has signed up for – phone, multiple lines, internet, online security, online backup, device * protection, tech support, and streaming TV and movies
• Customer account information – how long they’ve been a customer, contract, payment method, paperless billing, monthly charges, and total charges
• Demographic info about customers – gender, age range, and if they have partners and dependents

Columns	Description
customerID	Customer ID
gender	Whether the customer is a male or a female
SeniorCitizen	Whether the customer is a senior citizen or not (1, 0)
Partner	Whether the customer has a partner or not (Yes, No)
Dependents	Whether the customer has dependents or not (Yes, No)
tenure	Number of months the customer has stayed with the company
PhoneService	Whether the customer has a phone service or not (Yes, No)
MultipleLines	Whether the customer has multiple lines or not (Yes, No, No phone service)
InternetService	Customer’s internet service provider (DSL, Fiber optic, No)
OnlineSecurity	Whether the customer has online security or not (Yes, No, No internet service)
OnlineBackup	Whether the customer has an online backup or not (Yes, No, No internet service)
DeviceProtection	Whether the customer has device protection or not (Yes, No, No internet service)
TechSupport	Whether the customer has tech support or not (Yes, No, No internet service)
StreamingTV	Whether the customer has streaming TV or not (Yes, No, No internet service)
StreamingMovies	Whether the customer has streaming movies or not (Yes, No, No internet service)
Contract	The contract term of the customer (Month-to-month, One year, Two years)
PaperlessBilling	Whether the customer has paperless billing or not (Yes, No)
PaymentMethod	The customer’s payment method (Electronic check, Mailed check, Bank transfer (automatic), Credit card (automatic))
MonthlyCharges	The amount charged to the customer monthly
TotalCharges	The total amount charged to the customer
Churn	Whether the customer churned or not (Yes or No)

Data = pd.read_csv('telco-customer-churn/WA_Fn-UseC_-Telco-Customer-Churn.csv')

def Data_info(Inp, Only_NaN = False):
    Out = Inp.dtypes.to_frame(name='Data Type').sort_values(by=['Data Type'])
    Out = Out.join(Inp.isnull().sum().to_frame(name = 'Number of NaN Values'), how='outer')
    Out ['Size'] = Inp.shape[0]
    Out['Percentage'] = 100 - np.round(100*(Out['Number of NaN Values']/Inp.shape[0]),2)
    Out.index.name = 'Features'
    Out['Data Type'] = Out['Data Type'].astype(str)
    if Only_NaN:
        Out = Out.loc[Out['Number of NaN Values']>0]
    return Out

def text_sep(txt): return re.sub(r"(\w)([A-Z])", r"\1 \2", txt)

data_info = Data_info(Data).reset_index(drop = False)
fig = px.bar(data_info, x= 'Features', y= 'Percentage', color = 'Data Type', text = 'Data Type',
            color_discrete_sequence = ['PaleGreen', 'LightBlue', 'PeachPuff'], hover_data = data_info.columns)
fig.update_layout(plot_bgcolor= 'white', legend=dict(x=1, y=.5, traceorder="normal",
                                                     bordercolor="DarkGray", borderwidth=1), width = 980)
fig.update_traces(texttemplate= 6*' ' + '%{label}', textposition='inside')
fig.update_traces(marker_line_color= 'Black', marker_line_width=1., opacity=1)
fig.show()
   
Data.rename(columns = {'gender':'Gender', 'tenure':'Tenure'}, inplace = True)
Data.columns = [text_sep(txt) for txt in Data.columns.tolist()]

Exploratory Data Analysis

Customer Churn by Gender

Feature = 'Gender'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
display(Temp)
C = ['aquamarine', 'steelblue']
SC = 'Navy'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color= SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide', plot_bgcolor= 'white')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_layout(title={'text': 'Customer Churn by %s' % Feature,
                         'x':0.5, 'y':0.9,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color= SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Remaining', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Gender	Churn
Female	No	2549	36.19
	Yes	939	13.33
Male	No	2625	37.27
	Yes	930	13.20

What stands out from the graph is that nearly 27% of the customers churned, and there is a balance between the two genders among churned customers.

Customer Churn by Senior Citizen

Temp = Data.copy()
Temp['Senior Citizen'] = Temp['Senior Citizen'].map(lambda x: 'Yes' if x ==1 else 'No')
Feature = 'Senior Citizen'
Temp = Temp.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
display(Temp)
C = ['greenyellow', 'seagreen']
SC = 'DarkGreen'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide', plot_bgcolor= 'white')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title={'text': 'Customer Churn by %s' % Feature,
                         'x':0.5, 'y':0.9,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color=SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Remaining', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Senior Citizen	Churn
No	No	4508	64.01
	Yes	1393	19.78
Yes	No	666	9.46
	Yes	476	6.76

It can be seen that only 25.5% of the senior customers were churned.

Customer Churn by Senior Citizen and Gender

Temp = Data[['Gender','Senior Citizen','Churn']]
Temp['Senior Citizen'] = Temp['Senior Citizen'].map(lambda x: 'Yes' if x ==1 else 'No')
Temp = Temp.groupby(['Gender','Senior Citizen','Churn'])['Churn'].agg({'count'}).rename(columns = {'count':'Count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp = Temp[(Temp.T != 0).any()]
display(Temp)
Temp.reset_index(drop = False, inplace = True)

# Figures
C = ['greenyellow', 'seagreen']
SC = 'DarkGreen'

fig = make_subplots(rows=2, cols=1, vertical_spacing = 0.05, shared_xaxes=True,
                    subplot_titles=('Churned', 'Remaining'))
# Top
fig1 = px.bar(Temp.loc[Temp.Churn == 'Yes'], y= 'Senior Citizen', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = C, height= 200)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= SC, marker_line_width=1, opacity=1, row=1, col=1)

# Bottom
fig2 = px.bar(Temp.loc[Temp.Churn == 'Yes'], y= 'Senior Citizen', x= 'Percentage', orientation='h',
              color = 'Gender', text = 'Percentage', hover_data= Temp.columns,
              color_discrete_sequence = C, height= 200)

fig.add_trace(fig2['data'][0], row=2, col=1)
fig.add_trace(fig2['data'][1], row=2, col=1)
fig.update_traces(marker_line_color= SC, marker_line_width=1, opacity=1, showlegend = False, row=2, col=1)

# Update

fig.update_layout(height= 600)
fig.update_layout(plot_bgcolor= 'white', legend_orientation='h')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_traces(texttemplate='%{text:.2}%', textposition='outside')
fig.update_xaxes(title_text='Percent', range=[0, 20], row=2, col=1)
fig.update_yaxes(title_text='Senior Citizen', row=1, col=1)
fig.update_yaxes(title_text='Senior Citizen', row=2, col=1)
fig.update_layout(title={'text': 'Customer Churn by Senior Citizen and Gender',
                         'x':0.50, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

			Count	Percentage
Gender	Senior Citizen	Churn
Female	No	No	2221	31.53
		Yes	699	9.92
	Yes	No	328	4.66
		Yes	240	3.41
Male	No	No	2287	32.47
		Yes	694	9.85
	Yes	No	338	4.80
		Yes	236	3.35

Senior citizens churned much less than non-senior citizens in both genders.

Customer Churn by Partner

Feature = 'Partner'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['bisque', 'orange']
SC = 'DarkOrange'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide', plot_bgcolor= 'white')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title={'text': 'Customer Churn by %s' % Feature,
                         'x':0.5, 'y':0.9,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color=SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Remaining', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Partner	Churn
No	No	2441	34.66
	Yes	1200	17.04
Yes	No	2733	38.80
	Yes	669	9.50

Over 64% of churned customers did not have any partners.

Customer Churn by Dependents

Feature = 'Dependents'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['pink', 'hotpink']
SC = 'DarkRed'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 220)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide', plot_bgcolor= 'white')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title={'text': 'Customer Churn by %s' % Feature,
                         'x':0.5, 'y':0.9,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color=SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Remaining', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Dependents	Churn
No	No	3390	48.13
	Yes	1543	21.91
Yes	No	1784	25.33
	Yes	326	4.63

Over 82 percent of churned customers did not have any dependents.

Customer Churn by Tenure

Feature = 'Tenure'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage',color_continuous_scale= 'ylgn', height= 450)
fig.show()


C = ['violet', 'mediumorchid']
SC = 'Indigo'
fig = px.bar(Temp, x= Feature, y= 'Percentage',
             color = 'Churn', text = 'Percentage', color_discrete_sequence= C, height= 500)

fig.update_traces(marker_line_color=SC, marker_line_width=1.2, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide', plot_bgcolor= 'white')
fig['layout']['yaxis'].update(range=[0, 10])
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_layout(title={'text': 'Customer Churn by %s' % Feature,
                         'x':0.5, 'y':0.92,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()
del Feature

Customers with a higher tenure tend to churn less.

Customer Churn by Contract

Feature = 'Contract'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['greenyellow', 'limeGreen','DarkGreen']
SC = 'DarkGreen'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 240)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide', plot_bgcolor= 'white')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_layout(title={'text': 'Customer Churn by %s' % Feature,
                         'x':0.5, 'y':0.9,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color= SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Remaining', x=0.845, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Contract	Churn
Month-to-month	No	2220	31.52
	Yes	1655	23.50
One year	No	1307	18.56
	Yes	166	2.36
Two year	No	1647	23.38
	Yes	48	0.68

The majority of churned customers were on a month-to-month base contract.

Customer Churn by Payment Method

Feature = 'Payment Method'
Temp = Data.groupby([Feature,'Churn'])[Feature].agg({'count'})
Temp['Percentage'] = np.round(100* Temp.values /Temp.sum().values, 2)

display(Temp)
C = ['azure','paleturquoise','steelblue','MidnightBlue']
SC = 'Navy'
Temp.reset_index(drop = False, inplace = True)
fig = px.bar(Temp, y= 'Churn', x= 'Percentage', orientation='h',
             color = Feature, text = 'Percentage', color_discrete_sequence= C, height= 260)
fig.update_traces(marker_line_color=SC, marker_line_width=1.5, opacity=1)
fig.update_traces(texttemplate='%{text:.2}', textposition='inside')
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide', plot_bgcolor= 'white')
fig['layout']['xaxis'].update(range=[0, 100])
fig.update_layout(title={'text': 'Customer Churn by %s' % Feature,
                         'x':0.5, 'y':0.9,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.show()


fig = make_subplots(rows=1, cols=2, specs=[[{'type':'domain'}, {'type':'domain'}]])

fig.add_trace(go.Pie(labels= Temp.loc[Temp.Churn == 'Yes',Feature].values,
                     values= Temp.loc[Temp.Churn == 'Yes','count'].values,
                     name= 'Churn [Yes]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 1)

fig.add_trace(go.Pie(labels=Temp.loc[Temp.Churn == 'No', Feature].values,
                     values=Temp.loc[Temp.Churn == 'No','count'].values,
                     name= 'Churn [No]', textfont=dict(size=16),
                     marker=dict(colors = C, line=dict(color='black', width=1))), 1, 2)

fig.update_traces(hole=.6, marker_line_color= SC, marker_line_width=1, opacity=1)

fig.update_layout(legend_title=Feature, font=dict(size=14), legend=dict(orientation="v"),
                  annotations=[dict(text='Churned', x=0.18, y=0.5, font_size=16, showarrow=False),
                               dict(text='Remaining', x=0.85, y=0.5, font_size=16, showarrow=False)], height = 400)
fig.show()
del Feature

		count	Percentage
Payment Method	Churn
Bank transfer (automatic)	No	1286	18.26
	Yes	258	3.66
Credit card (automatic)	No	1290	18.32
	Yes	232	3.29
Electronic check	No	1294	18.37
	Yes	1071	15.21
Mailed check	No	1304	18.51
	Yes	308	4.37

Customers with an automatic payment method churned less.

Churn by Monthly Charges and Tenure

Temp = Data[['Monthly Charges', 'Tenure', 'Churn']]
Temp['Monthly Charges'] = Temp['Monthly Charges']
Temp['Tenure'] = pd.to_numeric(Temp['Tenure'], errors='coerce')
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
Temp['Tenure'] = imp.fit_transform(Temp['Tenure'].values.reshape(-1,1))

bins = pd.IntervalIndex.from_tuples([(18, 40), (40, 60), (60, 80),(80, 100), (100, 120)])
Temp['Monthly Charges'] = pd.cut(Temp['Monthly Charges'], bins)

bins = pd.IntervalIndex.from_tuples([(0,6), (6, 12), (12, 18), (18, 24), (24, 36), (36, 60), (60, 80)])
Temp['Tenure'] = pd.cut(Temp['Tenure'], bins)

del bins

fig = make_subplots(rows=1, cols=2, shared_yaxes=True,
                    subplot_titles=('Customer Churn by Monthly Charges',
                                    'Customer Churn by Tenure'))

C = ['LightCoral', 'LimeGreen']
SC = 'Black'
# Left
Temp0 = Temp.groupby(['Monthly Charges', 'Churn'])['Churn'].agg({'count'})
Temp0['Percentage'] = np.round(100* Temp0.values /Temp0.sum().values, 2)
Temp0 = Temp0.reset_index()
Temp0['Monthly Charges'] = Temp0['Monthly Charges'].astype(str)

fig1 = px.bar(Temp0, x= 'Monthly Charges', y= 'Percentage', color = 'Churn',
              text = 'Percentage', color_discrete_sequence= C)
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= SC, marker_line_width=.8, opacity=1, showlegend = False, row=1, col=1)

# Right
Temp0 = Temp.groupby(['Tenure', 'Churn'])['Churn'].agg({'count'})
Temp0['Percentage'] = np.round(100* Temp0.values /Temp0.sum().values, 2)
Temp0 = Temp0.reset_index()
Temp0['Tenure'] = Temp0['Tenure'].astype(str)

fig2 = px.bar(Temp0, x= 'Tenure', y= 'Percentage', color = 'Churn',
              text = 'Percentage', color_discrete_sequence= C)
fig2.update_traces(marker_line_color= SC, marker_line_width=1, opacity=1)
fig.add_trace(fig2['data'][0], row=1, col=2)
fig.add_trace(fig2['data'][1], row=1, col=2)

# Updates
fig.update_yaxes(title_text='Percentage', range=[0, 30], row=1, col=1)
fig.update_layout(barmode='stack', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.show()

What can be seen from this left plot is that as the monthly charges increase, the churn rate increases as well. However, it seems that customers with monthly charges around 100$ or above churn less than customers with a monthly charge of 80 to 100.

Moreover, we can see from the right plot that the churn rate decreases as tenure increases.

Alternatively, we can also demonstrate the above figure as follows

Temp = Data[['Monthly Charges', 'Tenure', 'Churn']]
Temp['Monthly Charges'] = Temp['Monthly Charges']
Temp['Tenure'] = pd.to_numeric(Temp['Tenure'], errors='coerce')
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
Temp['Tenure'] = imp.fit_transform(Temp['Tenure'].values.reshape(-1,1))

C = ['hotpink', 'steelblue']
SC = 'indigo'

fig = px.scatter(Temp, x= 'Monthly Charges', y= 'Tenure', color = 'Churn',
                  color_discrete_sequence= C, hover_data= Temp.columns, height= 600)
fig.update_traces(marker_line_color=SC, marker_line_width=0.5, opacity=1)
fig.update_layout(uniformtext_minsize= 8, uniformtext_mode='hide')
fig['layout']['xaxis'].update(range=[17, 120])
fig['layout']['yaxis'].update(range=[-1, 80])
fig.update_layout(title = 'Customer Churn by Gender', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray')
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray',
                 zeroline=True, zerolinewidth=1, zerolinecolor='Lightgray')
fig.update_layout(title={'text': 'Customer Churn by Monthly Charges and Tenure',
                         'x':0.5, 'y':0.95,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

fig = make_subplots(rows=1, cols=2, subplot_titles=('by Monthly Charges and Gender', 'by Tenure and Gender'))

Colors = ['LightCoral', 'LimeGreen']
LC = 'Black'
# Left
fig1 = px.box(Data, x='Gender', y='Monthly Charges', color='Churn',
          hover_data=['Gender','Monthly Charges','Churn'], color_discrete_sequence= Colors[::-1])
fig1.update_traces(quartilemethod='linear')
fig.add_trace(fig1['data'][0], row=1, col=1)
fig.add_trace(fig1['data'][1], row=1, col=1)
fig.update_traces(marker_line_color= LC, marker_line_width=.8, opacity=1, showlegend = False, row=1, col=1)

# # Right
fig2 = px.box(Data, x='Gender', y='Tenure', color='Churn',
          hover_data=['Gender','Tenure','Churn'], color_discrete_sequence= Colors[::-1])
fig2.update_traces(quartilemethod='linear')
fig.add_trace(fig2['data'][0], row=1, col=2)
fig.add_trace(fig2['data'][1], row=1, col=2)

# Updates
fig.update_layout(boxmode='group')
fig.update_yaxes(title_text='Percentage', range=[-2, 140], row=1, col=1)
fig.update_yaxes(title_text='Percentage', range=[-2, 80], row=1, col=2)
fig.update_layout(barmode='stack', plot_bgcolor= 'white')
fig.update_xaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True)
fig.update_yaxes(showline=True, linewidth=1, linecolor='Lightgray', mirror=True,
                 showgrid=True, gridwidth=1, gridcolor='Lightgray',
                 zeroline=True, zerolinewidth=1, zerolinecolor='Lightgray')

fig.update_layout(title={'text': 'Customer Churn',
                         'x':0.5, 'y':0.88,
                         'xanchor': 'center', 'yanchor': 'top'})
fig.show()

For classification and modeling, please see the next files in the directory.

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